Sunbelt 2025
Conference of the International Network for Social Network Analysis
Paris, June 23-29
Leveraging the power of big data represents an opportunity for researchers and managers to reveal patterns and trends in social and organizational behaviors. This workshop demonstrates how to successfully integrate Text Mining with Social Network Analysis for business and research applications. It introduces the Semantic Brand Score (SBS) and other advanced methods and tools for analyzing semantic networks, assessing brand/semantic importance, and
performing complex NLP tasks. Participants will also learn about network topic models and methods for measuring language novelty and impact, among other key techniques. The workshop highlights the functionalities of the SBS Business Intelligence App (SBS BI), which is designed to produce a wide range of analytics and mine textual data. Through several case studies, we show how these methods have been used, for example, to predict tourism trends, select advertising campaign testimonials, and make economic, financial, and political forecasts. SBS BI’s analytical power extends beyond “brands”, with applications that include:
commercial brands (e.g., Pepsi vs. Coke); products (e.g., pasta vs. pizza); personal brands (e.g., the name and image of political candidates); and concepts related to societal trends (e.g., terms used in media communication that shape public perceptions of the economy). By combining text analysis with network science, the workshop equips participants with tools that can transform decision-making and organizational management in the era of big data.
More info and materials are available at: https://learn.semanticbrandscore.com
Keywords: Semantic Brand Score, Text Mining, Social Network Analysis
This session focuses on cutting-edge research at the intersection of text analysis (including discourse analysis, content analysis, text mining, and natural language processing) and network analysis/network science. Work presented in this session explores the study of word networks, socio-semantic networks, and the representation of text-based information as graphs (e.g., knowledge graphs), along with the extraction of network data from text. Research on “Words and Networks” has resulted in prominent studies on topics such as language change, recommender systems, collaborative innovation, semantic computing, and the diffusion of (mis)information in both online and offline environments.
A growing field of interest is the role of artificial intelligence and large language models in understanding social interactions. These models are advancing research on language patterns and behaviors within networks, as well as their implications for social structures and interactions. Another key area is organizational communication, where improved insights into the impact of language within and across organizations have led to actions that enhance employee engagement and client relationships. In the domains of marketing and branding, the application of text mining and network analysis has transformed the understanding of consumer behavior, leading to more targeted communication strategies based on the use of words, ideas, and narratives as networked entities. Additionally, the integration of text mining and network analysis has proven valuable in identifying and connecting social agents, fostering community development, and studying social movements.
We invite abstract submissions that contribute to the integration of text analysis, network analysis, and AI, particularly those exploring new methods, applications, or theoretical approaches. Both basic and applied studies are encouraged.
Keywords: social network analysis, text mining
Key Dates:
Deadline submission abstracts 20 February 2025.